Literature DB >> 19176558

Retention time alignment algorithms for LC/MS data must consider non-linear shifts.

Katharina Podwojski1, Arno Fritsch, Daniel C Chamrad, Wolfgang Paul, Barbara Sitek, Kai Stühler, Petra Mutzel, Christian Stephan, Helmut E Meyer, Wolfgang Urfer, Katja Ickstadt, Jörg Rahnenführer.   

Abstract

MOTIVATION: Proteomics has particularly evolved to become of high interest for the field of biomarker discovery and drug development. Especially the combination of liquid chromatography and mass spectrometry (LC/MS) has proven to be a powerful technique for analyzing protein mixtures. Clinically orientated proteomic studies will have to compare hundreds of LC/MS runs at a time. In order to compare different runs, sophisticated preprocessing steps have to be performed. An important step is the retention time (rt) alignment of LC/MS runs. Especially non-linear shifts in the rt between pairs of LC/MS runs make this a crucial and non-trivial problem.
RESULTS: For the purpose of demonstrating the particular importance of correcting non-linear rt shifts, we evaluate and compare different alignment algorithms. We present and analyze two versions of a new algorithm that is based on regression techniques, once assuming and estimating only linear shifts and once also allowing for the estimation of non-linear shifts. As an example for another type of alignment method we use an established alignment algorithm based on shifting vectors that we adapted to allow for correcting non-linear shifts also. In a simulation study, we show that rt alignment procedures that can estimate non-linear shifts yield clearly better alignments. This is even true under mild non-linear deviations. AVAILABILITY: R code for the regression-based alignment methods and simulated datasets are available at http://www.statistik.tu-dortmund.de/genetik-publikationen-alignment.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Year:  2009        PMID: 19176558     DOI: 10.1093/bioinformatics/btp052

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  25 in total

1.  Retention time alignment of LC/MS data by a divide-and-conquer algorithm.

Authors:  Zhongqi Zhang
Journal:  J Am Soc Mass Spectrom       Date:  2012-04       Impact factor: 3.109

2.  Warpgroup: increased precision of metabolomic data processing by consensus integration bound analysis.

Authors:  Nathaniel G Mahieu; Jonathan L Spalding; Gary J Patti
Journal:  Bioinformatics       Date:  2015-09-30       Impact factor: 6.937

3.  Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics.

Authors:  Ute Distler; Jörg Kuharev; Pedro Navarro; Yishai Levin; Hansjörg Schild; Stefan Tenzer
Journal:  Nat Methods       Date:  2013-12-15       Impact factor: 28.547

4.  Maximizing peptide identification events in proteomic workflows using data-dependent acquisition (DDA).

Authors:  Nicholas W Bateman; Scott P Goulding; Nicholas J Shulman; Avinash K Gadok; Karen K Szumlinski; Michael J MacCoss; Christine C Wu
Journal:  Mol Cell Proteomics       Date:  2013-07-02       Impact factor: 5.911

5.  Profile-Based LC-MS data alignment--a Bayesian approach.

Authors:  Tsung-Heng Tsai; Mahlet G Tadesse; Yue Wang; Habtom W Ressom
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2013 Mar-Apr       Impact factor: 3.710

6.  An adaptive alignment algorithm for quality-controlled label-free LC-MS.

Authors:  Marianne Sandin; Ashfaq Ali; Karin Hansson; Olle Månsson; Erik Andreasson; Svante Resjö; Fredrik Levander
Journal:  Mol Cell Proteomics       Date:  2013-01-09       Impact factor: 5.911

7.  Label-free quantification in ion mobility-enhanced data-independent acquisition proteomics.

Authors:  Ute Distler; Jörg Kuharev; Pedro Navarro; Stefan Tenzer
Journal:  Nat Protoc       Date:  2016-03-24       Impact factor: 13.491

Review 8.  Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites.

Authors:  Brett C Covington; John A McLean; Brian O Bachmann
Journal:  Nat Prod Rep       Date:  2017-01-04       Impact factor: 13.423

9.  CAMS-RS: Clustering Algorithm for Large-Scale Mass Spectrometry Data Using Restricted Search Space and Intelligent Random Sampling.

Authors:  Fahad Saeed; Jason D Hoffert; Mark A Knepper
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2014 Jan-Feb       Impact factor: 3.710

10.  Visualization, Quantification, and Alignment of Spectral Drift in Population Scale Untargeted Metabolomics Data.

Authors:  Jeramie D Watrous; Mir Henglin; Brian Claggett; Kim A Lehmann; Martin G Larson; Susan Cheng; Mohit Jain
Journal:  Anal Chem       Date:  2017-01-26       Impact factor: 6.986

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